Emission reduction path optimization method and system based on information entropy

Abstract

The invention discloses an emission reduction path optimization method and system based on information entropy, relates to the technical field of emission reduction decision, and aims to solve the problem that the selection of an emission reduction path lacks a comprehensive evaluation means, and the method comprises the steps: obtaining emission reduction information to determine an emission reduction path index, and constructing a decision matrix D according to the membership degree and the non-membership degree of the index; normalizing the quantitative index data, and calculating to obtain a normative decision matrix T; according to the decision matrix D and the standard decision matrix T, constructing a first membership matrix and calculating an information entropy, and further determining an index weight; and determining an intuitive fuzzy positive ideal solution and a negative ideal solution, calculating the relative proximity of the emission reduction paths and the positive ideal solution, sorting the emission reduction paths according to the relative proximity, and generating an optimization scheme. According to the method, the final decision scheme and optimization scheme can be determined according to the relative proximity of the emission reduction path and the positive ideal solution, the emission reduction path is comprehensively evaluated, and the accuracy and rationality of selection of the emission reduction scheme can be ensured.

Description

Technical Field

[0001] This invention relates to the field of emission reduction decision-making technology, and specifically to an emission reduction path optimization method and system based on information entropy. Background Technology

[0002] Currently, the selection of emission reduction paths is usually determined based on the subjective factors of decision-makers, or by analyzing only a few individual factors of the emission reduction plan. These methods do not conduct a comprehensive evaluation of the emission reduction plan based on commonly used emission reduction technologies, which leads to uncertainty and inaccuracy in the selection of emission reduction plans and is not conducive to scientific emission reduction decision-making.

[0003] For example, Chinese patent CN118261303A discloses a method, device, and storage medium for optimizing a large-scale carbon emission reduction scheme model. The method includes: obtaining a sample dataset of the optimization process for a preferred carbon emission reduction scheme; the sample dataset of the optimization process consists of data from the processing steps that generate the preferred carbon emission reduction scheme; forming a training sample dataset based on the preferred carbon emission reduction scheme and the sample dataset of the optimization process; and optimizing the parameters of a preset large-scale model based on the training sample dataset to obtain a target large-scale carbon emission reduction scheme model, thus optimizing the processing capability of the large-scale carbon emission reduction scheme model. However, Chinese patent CN118261303A only involves parameter optimization of the model and does not allow for the selection of multiple emission reduction paths. Summary of the Invention

[0004] This invention addresses the lack of comprehensive evaluation methods for selecting emission reduction paths. It proposes an emission reduction path optimization method and system based on information entropy, which quantitatively analyzes emission reduction path indicators, calculates information entropy and indicator weights through a corresponding decision matrix, and finally determines the final decision scheme and optimization scheme based on the relative proximity of the emission reduction path to the positive ideal solution. This provides a comprehensive evaluation of emission reduction paths and ensures the accuracy and rationality of emission reduction scheme selection.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An emission reduction path optimization method based on information entropy includes the following steps: S1. Obtain emission reduction information to determine emission reduction path indicators, and construct a decision matrix D based on the membership and non-membership of the indicators; S2. Normalize the quantitative indicator data and calculate the normalized decision matrix T. S3. Construct the first membership matrix based on the decision matrix D and the normalized decision matrix T, calculate the information entropy, and then determine the index weights. S4. Determine the intuitionistic fuzzy positive ideal solution and negative ideal solution, calculate the relative proximity between the emission reduction path and the positive ideal solution, sort the emission reduction paths according to the relative proximity and generate an optimized scheme.

[0006] In this technical solution, firstly, the qualitative indicators of the corresponding emission reduction paths are determined. Then, a decision matrix is ​​calculated and constructed based on the membership and non-membership degrees of the indicators. Subsequently, the quantitative indicator data is normalized, and a normalized decision matrix is ​​constructed. Then, a first membership matrix is ​​established based on the decision matrix and the normalized decision matrix. The information entropy is calculated based on the new first membership matrix, and the indicator weights are calculated. Finally, the relative closeness between the emission reduction paths and the positive ideal solution is calculated, and the emission reduction paths are ranked, and decisions and optimizations are made.

[0007] The present invention is further configured such that step S1 includes: S11, collect relevant information on commonly used emission reduction technologies and determine several indicators related to emission reduction pathways; S12, Construct a mapping relationship between linguistic terms and intuitive fuzzy coefficients in emission reduction path indicators; S13, construct the decision matrix D based on the intuitionistic fuzzy coefficients and calculate the uncertainty coefficients.

[0008] In this technical solution, after determining the emission reduction information, several qualitative indicators for emission reduction paths are determined. Then, several linguistic terms and corresponding intuitionistic fuzzy coefficients for qualitative evaluation of the emission reduction path indicators are determined. The intuitionistic fuzzy coefficients include membership degree and non-membership degree. Finally, a decision matrix D is constructed based on the above membership degree and non-membership degree.

[0009] The present invention is further configured such that step S2 specifically includes: The quantitative index data are normalized to obtain the membership degree of the quantitative index data, and the non-membership degree of the quantitative index data is calculated based on the uncertainty coefficient. Finally, the normalized decision matrix T is obtained by combining the results.

[0010] In this technical solution, the corresponding membership degree and non-membership degree are obtained by standardizing the quantitative index data, so as to obtain the standardized decision matrix.

[0011] The present invention is further configured such that step S3 includes: S31, merge decision matrix D1 and normalized decision matrix T, and calculate the new membership contribution and non-membership contribution; S32, calculate the total contribution of multiple alternatives to this indicator; S33. The importance of indicators is determined based on information entropy, and the corresponding indicator weights are calculated based on the importance of indicators.

[0012] In this technical solution, the decision matrix and the standard decision matrix are first merged to calculate a new membership contribution degree and non-membership contribution degree, and then a membership degree matrix is ​​constructed. Subsequently, the information entropy, that is, the total contribution of multiple alternatives to the indicator, is calculated. Finally, the indicator weight is calculated through the indicator importance to achieve the purpose of determining the indicator weight based on information entropy.

[0013] The present invention is further configured such that step S4 includes: S41, determine the positive and negative ideal solutions of intuitionistic fuzzy based on the weighted intuitionistic fuzzy values; S42, calculate the separation degree between the emission reduction path and the positive and negative ideal solutions; S44. Calculate the relative proximity between the emission reduction path and the positive ideal solution, and sort the emission reduction paths from largest to smallest according to the relative proximity.

[0014] In this technical solution, the weighted intuitionistic fuzzy decision matrix is ​​first calculated, and then the weighted intuitionistic fuzzy value is determined. The intuitionistic fuzzy positive ideal solution A+ and the intuitionistic fuzzy negative ideal solution A- are determined by the weighted intuitionistic fuzzy value. Then, the separation degree between the emission reduction path and the above-mentioned intuitionistic fuzzy positive ideal solution A+ and intuitionistic fuzzy negative ideal solution A- is calculated, and the relative proximity degree is calculated based on the separation degree.

[0015] The present invention further specifies that the membership degree and non-membership degree of the quantitative index data are respectively represented as: i = 1, 2, ..., m; j = 1, 2, ..., n v ij =1-u ij In the formula: x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of quantitative indicators, respectively.

[0016] In this technical solution, the uncertainty coefficient π of the quantitative index ij It is 0.

[0017] The present invention further specifies that the index weight is represented as: Where, d i =1-E i d i For the i-th index C i The importance of the indicator, E i For the i-th index C i Information entropy, expressed as: Where, p ij Represents the i-th attribute and the j-th scheme A. j Subordinate contribution degree; q ij Represents the i-th attribute and the j-th scheme A. j Non-affiliated contribution.

[0018] In this technical solution, the aforementioned E i The range is 0 ≤ E i ≤1. Specifically, when p ij =q ij When = 1 / n, E i =1. That is, when the membership contribution and non-membership contribution of each option under a certain indicator tend to be consistent, E i It tends to 1.

[0019] The present invention is further configured such that: the intuitionistic fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - Represented as: Where I is the set of benefit-type attributes and J is the set of cost-type attributes.

[0020] In this technical solution, after determining the aforementioned intuitionistic fuzzy positive ideal solution A... + And intuitive fuzzy negative ideal solution A - This will prepare for subsequent calculations of separation and relative proximity.

[0021] The present invention further specifies that the separation degree between the emission reduction path and the positive ideal solution and the negative ideal solution is expressed as: The relative proximity between the emission reduction path and the positive ideal solution is expressed as: Where, r i This refers to relative proximity.

[0022] In this technical solution, emission reduction paths are sorted from largest to smallest based on the calculated relative proximity, providing data support for optimizing the solution.

[0023] An emission reduction path optimization system based on information entropy, employing the aforementioned emission reduction path optimization method based on information entropy, includes: The decision matrix construction module constructs the decision matrix D based on the membership and non-membership degrees of the indicators. The decision matrix normalization module normalizes quantitative indicator data and obtains a normalized decision matrix T. The indicator weight determination module calculates the information entropy and determines the indicator weights. The decision support module calculates the relative proximity between emission reduction paths and ideal solutions, and sorts the emission reduction paths according to the relative proximity.

[0024] In this technical solution, the decision matrix construction module is connected to the decision matrix standardization module, the decision matrix standardization module is connected to the indicator weight determination module, and the indicator weight determination module is connected to the decision support module. The above four modules can process and calculate the corresponding types of data in sequence to ensure the subsequent generation of optimized emission reduction path schemes.

[0025] The present invention can bring the following beneficial effects: This invention relates to an emission reduction path optimization method based on information entropy, which quantitatively analyzes emission reduction path indicators, calculates information entropy and indicator weights through a corresponding decision matrix, and finally determines the final decision scheme and optimization scheme based on the relative closeness of the emission reduction path to the positive ideal solution, so as to comprehensively evaluate the emission reduction path and ensure the accuracy and rationality of the emission reduction scheme selection. The present invention relates to an emission reduction path optimization system based on information entropy, wherein the decision matrix construction module, decision matrix standardization module, indicator weight determination module, and decision support module can process and calculate corresponding types of data in sequence to ensure the subsequent generation of optimized emission reduction path schemes. Attached Figure Description

[0026] Figure 1 This is a flowchart of an emission reduction path optimization method based on information entropy proposed in this application.

[0027] Figure 2 This is a simplified schematic diagram of an emission reduction path optimization system based on information entropy, as described in this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0029] Example 1 This embodiment proposes an emission reduction path optimization method based on information entropy, referring to... Figure 1 and Figure 2 It mainly includes the following steps.

[0030] Step S1: First, obtain emission reduction information, then determine emission reduction path indicators, and finally construct a decision matrix D based on the membership degree and non-membership degree of the indicators.

[0031] Step S1 above mainly includes the following sub-steps.

[0032] Step S11: First, collect relevant information on commonly used emission reduction technologies and determine several indicators related to emission reduction pathways.

[0033] Step S12: Construct a mapping relationship between linguistic terms and intuitive fuzzy coefficients in the emission reduction path indicators.

[0034] For step S12, the linguistic terms for qualitative evaluation of the given emission reduction path indicators, and the corresponding intuitive fuzzy coefficients.

[0035] As a preferred implementation, linguistic terms are associated with intuitionistic fuzzy coefficients, which include membership and non-membership degrees. For details, please refer to Table 1. Table 1. Linguistic terms and corresponding intuitive fuzzy coefficients Language item Intuitive fuzzy coefficients (membership degree, non-membership degree) Very bad (0.02，0.98) Difference (0.15，0.75) Lower than average (0.35，0.55) medium (0.50，0.35) Above average (0.65，0.25) good (0.75，0.15) very good (0.98，0.02) .

[0036] In Table 1 above, when the linguistic term is "difference", the membership degree of the intuitionistic fuzzy coefficient is 0.15, and the non-membership degree is 0.75. There is also an uncertainty factor of 0.1, which is the uncertainty coefficient.

[0037] Given the defined linguistic terms and corresponding intuitionistic fuzzy coefficients, the intuitionistic fuzzy TOPSIS method based on information entropy is used for evaluation.

[0038] Step S13: Construct decision matrix D based on intuitionistic fuzzy coefficients and calculate uncertainty coefficients.

[0039] More specifically, step S13 mentioned above includes the following process: Define A = {A1, A2, ..., A} n Let C = {C1, C2, ..., C} be the set of possible solutions. m} is the set of indicators, therefore the decision matrix D is: D = [k ij ] m×n In the above formula, k ij For intuitive fuzzy values, define k ij =(u ij ,v ij ), where u ij and v ij For A respectively j Indicator C in the plan iThe degree of satisfaction (membership) and the degree of dissatisfaction (non-membership).

[0040] Subsequently, the uncertainty coefficient π is defined. ij For decision-makers regarding A j In the scheme C i The fuzziness or uncertainty in the judgment of indicators, the uncertainty coefficient π ij The calculation method is as follows: π ij =1-u ij -v ij .

[0041] In the steps described above, in order to determine the quantitative and qualitative indicators and the weighting relationships between different qualitative indicators, it is necessary to determine qualitative indicators, including safety, technological maturity, and social acceptance, based on the collection of relevant information on commonly used carbon reduction technologies.

[0042] Step S2: Based on the completion of step S1, the quantitative indicator data is normalized, and the normalized decision matrix T is finally calculated.

[0043] Step S21 above specifically includes the following process: The quantitative index data is normalized, and the membership degree of the quantitative index data is obtained. The non-membership degree of the quantitative index data is calculated by combining the uncertainty coefficient. Finally, the corresponding normalized decision matrix T of the German and Austrian countries is calculated.

[0044] In the above technical solution, the corresponding membership degree and non-membership degree are obtained by standardizing the quantitative index data, so as to obtain the standardized decision matrix.

[0045] The formula for standardizing quantitative indicator data and calculating the corresponding membership and non-membership degrees is as follows: i = 1, 2, ..., m; j = 1, 2, ..., n v ij =1-u ij In the above formula, x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of the quantitative indicators, respectively; in this embodiment, the hesitation index π of the quantitative indicators... ij It is 0.

[0046] In the above technical solution, after determining the emission reduction information, qualitative indicators of several emission reduction paths are determined. Then, linguistic terms and corresponding intuitionistic fuzzy coefficients are determined for the qualitative evaluation of several emission reduction path indicators. The intuitionistic fuzzy coefficients include membership degree and non-membership degree. Finally, a decision matrix D is constructed based on the above membership degree and non-membership degree.

[0047] After completing step S2, proceed to step S3, construct the first membership matrix based on the decision matrix D and the normalized decision matrix T, calculate the information entropy, and finally determine the index weights.

[0048] Indicator weights are determined based on information entropy. In multi-attribute decision-making problems, the inherent information of each option in the multi-attribute decision evaluation can be used to obtain the information entropy of each attribute. The smaller the information entropy, the lower the disorder of the information, and the greater the utility value of the information; in this case, the greater the weight of the attribute. Conversely, the larger the information entropy, the higher the disorder of the information, and the smaller the utility value of the information; thus, the smaller the weight of the attribute.

[0049] Step S3 mainly includes the following sub-steps.

[0050] Step S31: Merge decision matrix D1 and normalized decision matrix T to calculate new membership contribution and non-membership contribution.

[0051] For step S31 above, a membership matrix can be constructed. Specifically, the merged decision matrix D and the normalized decision matrix T are merged, and new membership and non-membership contributions are calculated. The calculation formula is as follows: In the formula: p ij Represents the i-th attribute and the j-th scheme A. j Subordinate contribution degree; q ij Represents the i-th attribute and the j-th scheme A. j Non-affiliated contribution.

[0052] Step S32: Calculate the total contribution of multiple alternative solutions to the indicator, i.e., calculate the information entropy.

[0053] For step S32, in more detail, it mainly includes the following process: Calculate information entropy: E i For the i-th index C i Information entropy, representing the information entropy of n alternative solutions A n For the i-th index C i The total contribution. The calculation formula is as follows: In conclusion, 0 ≤ E i ≤1. Specifically, when p ij=q ij When = 1 / n, E i =1. That is, when the membership contribution and non-membership contribution of each option under a certain indicator tend to be consistent, E i The weight of the indicator tends to 1. In particular, when the contribution of subordinates is equal and the contribution of non-subordinates is also equal, the role of the indicator in decision-making can be disregarded, that is, the weight of the indicator should be 0 at this time.

[0054] Step S33: Determine the importance of the indicator based on the information entropy, and calculate the corresponding indicator weight based on the indicator importance.

[0055] Step S33 mainly includes the following processes: Determine indicator weights: Set d i =1-E i For the i-th index C i If the importance of the indicator is such that the i-th indicator C is... i The formula for calculating the indicator weights is as follows:

[0056] In the above technical solution, the decision matrix and the standard decision matrix are first merged to calculate the new membership contribution and non-membership contribution, and then a membership matrix is ​​constructed. Subsequently, the information entropy, that is, the total contribution of multiple alternatives to the indicator, is calculated. Finally, the indicator weight is calculated through the indicator importance to achieve the purpose of determining the indicator weight based on information entropy.

[0057] After completing step S3, proceed to step S4 to calculate the intuitionistic fuzzy positive ideal solution and negative ideal solution respectively. Then, calculate the relative proximity between the emission reduction path and the positive ideal solution. Finally, sort the emission reduction paths according to the relative proximity and generate an optimized scheme.

[0058] In the above technical solution, the weighted intuitionistic fuzzy decision matrix is ​​first calculated, and then the weighted intuitionistic fuzzy value is determined. The intuitionistic fuzzy positive ideal solution A+ and the intuitionistic fuzzy negative ideal solution A- are determined by the weighted intuitionistic fuzzy value. Then, the separation degree between the emission reduction path and the above-mentioned intuitionistic fuzzy positive ideal solution A+ and intuitionistic fuzzy negative ideal solution A- is calculated, and the relative proximity degree is calculated based on the separation degree.

[0059] Step S4 mainly includes the following sub-steps.

[0060] Step S41: First, calculate the weighted intuitionistic fuzzy decision matrix, then determine the positive and negative intuitionistic fuzzy ideal solutions based on the weighted intuitionistic fuzzy values. For step S41 above, the weighted intuitionistic fuzzy decision matrix is ​​first calculated; the weighted intuitionistic fuzzy value is expressed as: Where, ω i The index C determined using the information entropy method i The weight.

[0061] Based on the above steps, determine the intuitionistic fuzzy positive ideal solution A. + And intuitive fuzzy negative ideal solution A - .

[0062] The above-mentioned intuitive fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - They are represented as follows: In the two formulas above, I represents the set of benefit-type attributes, and J represents the set of cost-type attributes.

[0063] In the above technical solution, the aforementioned intuitionistic fuzzy positive ideal solution A was determined. + And intuitive fuzzy negative ideal solution A - This will prepare for subsequent calculations of separation and relative proximity.

[0064] Step S42: Calculate the separation degree between the emission reduction path and the positive and negative ideal solutions based on the results of step S41.

[0065] For step S42 above, after completing step S41, the separation degree between the emission reduction path and the positive ideal solution and the negative ideal solution is calculated. The specific calculation formula is as follows:

[0066] Step S44: Calculate the relative proximity between the emission reduction path and the ideal solution, and finally sort the emission reduction paths from largest to smallest according to the relative proximity.

[0067] For step S44 above, the relative proximity is specifically expressed as follows: In the above formula, r i This refers to relative proximity.

[0068] In the above technical solution, the emission reduction paths are sorted from largest to smallest based on the calculated relative proximity, providing data support for the optimization scheme.

[0069] In the above embodiments, firstly, the qualitative indicators of the corresponding emission reduction paths are determined, and then a decision matrix is ​​calculated and constructed based on the membership and non-membership degrees of the indicators. Subsequently, the quantitative indicator data is normalized, and a normalized decision matrix is ​​constructed. Then, a first membership matrix is ​​established based on the decision matrix and the normalized decision matrix. The information entropy is calculated based on the new first membership matrix, and the indicator weights are calculated. Finally, the relative closeness between the emission reduction paths and the positive ideal solution is calculated, and the emission reduction paths are ranked, and decisions and optimizations are made.

[0070] Example 2 The emission reduction path optimization method based on information entropy as described in Example 1 mainly includes the following steps.

[0071] Step S1: First, obtain emission reduction information, then determine emission reduction path indicators, and finally construct a decision matrix D based on the membership degree and non-membership degree of the indicators.

[0072] Step S1 above mainly includes the following sub-steps.

[0073] Step S11: First, collect relevant information on commonly used emission reduction technologies and determine several indicators related to emission reduction pathways.

[0074] Step S12: Construct a mapping relationship between linguistic terms and intuitive fuzzy coefficients in the emission reduction path indicators.

[0075] For step S12, the linguistic terms for qualitative evaluation of the given emission reduction path indicators, and the corresponding intuitive fuzzy coefficients.

[0076] In Table 1 above, when the linguistic term is "difference", the membership degree of the intuitionistic fuzzy coefficient is 0.15, and the non-membership degree is 0.75. There is also an uncertainty factor of 0.1, which is the uncertainty coefficient.

[0077] Given the defined linguistic terms and corresponding intuitionistic fuzzy coefficients, the intuitionistic fuzzy TOPSIS method based on information entropy is used for evaluation.

[0078] Step S13: Construct decision matrix D based on intuitionistic fuzzy coefficients and calculate uncertainty coefficients.

[0079] More specifically, step S13 mentioned above includes the following process: Define A = {A1, A2, ..., A} n Let C = {C1, C2, ..., C} be the set of possible solutions. m} is the set of indicators, therefore the decision matrix D is: D = [k ij ] m×n In the above formula, kij For intuitive fuzzy values, define k ij =(u ij ,v ij ), where u ij and v ij For A respectively j Indicator C in the plan i The degree of satisfaction (membership) and the degree of dissatisfaction (non-membership).

[0080] Subsequently, the uncertainty coefficient π is defined. ij For decision-makers regarding A j In the scheme C i The fuzziness or uncertainty in the judgment of indicators, the uncertainty coefficient π ij The calculation method is as follows: π ij =1-u ij -v ij .

[0081] In the steps described above, in order to determine the quantitative and qualitative indicators and the weighting relationships between different qualitative indicators, it is necessary to determine qualitative indicators, including safety, technological maturity, and social acceptance, based on the collection of relevant information on commonly used carbon reduction technologies.

[0082] Step S2: Based on the completion of step S1, the quantitative indicator data is normalized, and the normalized decision matrix T is finally calculated.

[0083] Step S21 above specifically includes the following process: The quantitative index data is normalized, and the membership degree of the quantitative index data is obtained. The non-membership degree of the quantitative index data is calculated by combining the uncertainty coefficient. Finally, the corresponding normalized decision matrix T of the German and Austrian countries is calculated.

[0084] In the above technical solution, the corresponding membership degree and non-membership degree are obtained by standardizing the quantitative index data, so as to obtain the standardized decision matrix.

[0085] The formula for standardizing quantitative indicator data and calculating the corresponding membership and non-membership degrees is as follows: v ij =1-u ij In the above formula, x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of the quantitative indicators, respectively; in this embodiment, the hesitation index π of the quantitative indicators...ij It is 0.

[0086] In the above technical solution, after determining the emission reduction information, qualitative indicators of several emission reduction paths are determined. Then, linguistic terms and corresponding intuitionistic fuzzy coefficients are determined for the qualitative evaluation of several emission reduction path indicators. The intuitionistic fuzzy coefficients include membership degree and non-membership degree. Finally, a decision matrix D is constructed based on the above membership degree and non-membership degree.

[0087] After completing step S2, proceed to step S3, construct the first membership matrix based on the decision matrix D and the normalized decision matrix T, calculate the information entropy, and finally determine the index weights.

[0088] Indicator weights are determined based on information entropy. In multi-attribute decision-making problems, the inherent information of each option in the multi-attribute decision evaluation can be used to obtain the information entropy of each attribute. The smaller the information entropy, the lower the disorder of the information, and the greater the utility value of the information; in this case, the greater the weight of the attribute. Conversely, the larger the information entropy, the higher the disorder of the information, and the smaller the utility value of the information; thus, the smaller the weight of the attribute.

[0089] Step S3 mainly includes the following sub-steps.

[0090] Step S31: Merge decision matrix D1 and normalized decision matrix T to calculate new membership contribution and non-membership contribution.

[0091] For step S31 above, a membership matrix can be constructed. Specifically, the merged decision matrix D and the normalized decision matrix T are merged, and new membership and non-membership contributions are calculated. The calculation formula is as follows: In the formula: p ij Represents the i-th attribute and the j-th scheme A. j Subordinate contribution degree; q ij Represents the i-th attribute and the j-th scheme A. j Non-affiliated contribution.

[0092] Step S32: Calculate the total contribution of multiple alternative solutions to the indicator, i.e., calculate the information entropy.

[0093] For step S32, in more detail, it mainly includes the following process: Calculate information entropy: E i For the i-th index C i Information entropy, representing the information entropy of n alternative solutions A n For the i-th index C i The total contribution. The calculation formula is as follows: In conclusion, 0 ≤ E i ≤1. Specifically, when p ij =q ij When = 1 / n, E i =1. That is, when the membership contribution and non-membership contribution of each option under a certain indicator tend to be consistent, E i The weight of the indicator tends to 1. In particular, when the contribution of subordinates is equal and the contribution of non-subordinates is also equal, the role of the indicator in decision-making can be disregarded, that is, the weight of the indicator should be 0 at this time.

[0094] Step S33: Determine the importance of the indicator based on the information entropy, and calculate the corresponding indicator weight based on the indicator importance.

[0095] Step S33 mainly includes the following processes: Determine indicator weights: Set d i =1-E i For the i-th index C i If the importance of the indicator is such that the i-th indicator C is... i The formula for calculating the indicator weights is as follows:

[0096] In the above technical solution, the decision matrix and the standard decision matrix are first merged to calculate the new membership contribution and non-membership contribution, and then a membership matrix is ​​constructed. Subsequently, the information entropy, that is, the total contribution of multiple alternatives to the indicator, is calculated. Finally, the indicator weight is calculated through the indicator importance to achieve the purpose of determining the indicator weight based on information entropy.

[0097] After completing step S3, proceed to step S4 to calculate the intuitionistic fuzzy positive ideal solution and negative ideal solution respectively. Then, calculate the relative proximity between the emission reduction path and the positive ideal solution. Finally, sort the emission reduction paths according to the relative proximity and generate an optimized scheme.

[0098] In the above technical solution, the weighted intuitionistic fuzzy decision matrix is ​​first calculated, and then the weighted intuitionistic fuzzy value is determined. The intuitionistic fuzzy positive ideal solution A+ and the intuitionistic fuzzy negative ideal solution A- are determined by the weighted intuitionistic fuzzy value. Then, the separation degree between the emission reduction path and the above-mentioned intuitionistic fuzzy positive ideal solution A+ and intuitionistic fuzzy negative ideal solution A- is calculated, and the relative proximity degree is calculated based on the separation degree.

[0099] Step S4 mainly includes the following sub-steps.

[0100] Step S41: First, calculate the weighted intuitionistic fuzzy decision matrix, then determine the positive and negative intuitionistic fuzzy ideal solutions based on the weighted intuitionistic fuzzy values. For step S41 above, the weighted intuitionistic fuzzy decision matrix is ​​first calculated; the weighted intuitionistic fuzzy value is expressed as: Where, ω i The index C determined using the information entropy method i The weight.

[0101] Based on the above steps, determine the intuitionistic fuzzy positive ideal solution A. + And intuitive fuzzy negative ideal solution A - .

[0102] The above-mentioned intuitive fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - They are represented as follows: In the two formulas above, I represents the set of benefit-type attributes, and J represents the set of cost-type attributes.

[0103] In the above technical solution, the aforementioned intuitionistic fuzzy positive ideal solution A was determined. + And intuitive fuzzy negative ideal solution A - This will prepare for subsequent calculations of separation and relative proximity.

[0104] Step S42: Calculate the separation degree between the emission reduction path and the positive and negative ideal solutions based on the results of step S41.

[0105] For step S42 above, after completing step S41, the separation degree between the emission reduction path and the positive ideal solution and the negative ideal solution is calculated. The specific calculation formula is as follows:

[0106] Step S44: Calculate the relative proximity between the emission reduction path and the ideal solution, and finally sort the emission reduction paths from largest to smallest according to the relative proximity.

[0107] For step S44 above, the relative proximity is specifically expressed as follows: In the above formula, r i This refers to relative proximity.

[0108] In the above technical solution, the emission reduction paths are sorted from largest to smallest based on the calculated relative proximity, providing data support for the optimization scheme.

[0109] Based on the above, this embodiment also proposes an emission reduction path optimization system based on information entropy, referring to... Figure 2It mainly includes a decision matrix construction module, a decision matrix standardization module, an indicator weight determination module, and a decision support module.

[0110] The main function of the decision matrix construction module is to construct the decision matrix D, specifically, based on the membership and non-membership pairs of indicators. More specifically, this decision matrix construction module can be referenced to step S1 in the aforementioned information entropy-based emission reduction path optimization method.

[0111] The main function of the decision matrix normalization module is to normalize quantitative indicator data and integrate it to obtain a normalized decision matrix T. For a more detailed explanation of the decision matrix normalization module, please refer to step S2 in the aforementioned emission reduction path optimization method based on information entropy.

[0112] The main functions of the indicator weight determination module are: constructing a membership matrix, calculating the information entropy, and finally calculating the indicator weights. For a more detailed explanation of the indicator weight determination module, please refer to step S3 in the aforementioned information entropy-based emission reduction path optimization method.

[0113] The decision support module primarily performs the following functions: calculating the relative proximity between emission reduction paths and the ideal solution, and ranking the emission reduction paths based on the relative proximity. More specifically, for the decision support module, refer to step S4 in the aforementioned emission reduction path optimization method based on information entropy.

[0114] In the above technical solution, the decision matrix construction module and the decision matrix standardization module are both connected to the indicator weight determination module, and the indicator weight determination module is connected to the decision support module. The above four modules can process and calculate the corresponding types of data in sequence to ensure the subsequent generation of optimized emission reduction path schemes.

[0115] Example 3 Based on Example 1 or Example 2, emission reduction potential, emission reduction cost, and adoption rate were collected as quantitative indicators. These were then combined with the technical characteristics and practical application of the emission reduction technologies to evaluate the five types of emission reduction technologies. Table 2 shows the evaluation indicators for the five types of emission reduction technologies: Table 2 Evaluation Indicators for Five Types of Emission Reduction Technologies

[0116] In the table above, some technologies have negative emission reduction costs because, compared to the baseline technology, a certain emission reduction technology can generate net economic benefits or achieve emission reductions over its life cycle; positive costs represent the additional costs required to obtain emission reduction potential relative to the baseline technology.

[0117] Based on the normalization of quantitative indicator data and the conversion of qualitative indicator linguistic terms into intuitionistic fuzzy coefficients, the weights of each indicator are calculated using information entropy. According to the membership matrix provided in Table 3, the information entropy, importance, and weight of each indicator can be calculated (as shown in Table 4). Among them, emission reduction potential (C1) has the lowest information entropy, indicating the lowest information disorder and the highest information utility value; therefore, this indicator has the highest weight of 0.4713. Conversely, emission reduction cost has the highest information entropy and the lowest information utility value; therefore, this indicator has the lowest weight of 0.0403. Tables 3 and 4 are as follows: Table 3 Membership Matrix Table 4 Information Entropy and Indicator Weights C1 C2 C3 C4 C5 C6 C7 C8 <![CDATA[E i ]]> 0.8595 0.9880 0.9860 0.9646 0.9659 0.9852 0.9837 0.9690 <![CDATA[d i ]]> 0.1405 0.0120 0.0140 0.0354 0.0341 0.0148 0.0163 0.0310 <![CDATA[ω i ]]> 0.4713 0.0403 0.0471 0.1188 0.1143 0.0496 0.0547 0.1040 .

[0118] Based on the calculated indicator weights, a weighted intuitionistic fuzzy decision matrix is ​​constructed, where C2 and C5 are cost-type indicators, and C1, C3, C4, C6, C7, and C8 are benefit-type indicators. Subsequently, the intuitionistic fuzzy positive and negative ideal solutions for the indicators are determined, as shown in Tables 5 and 6. Table 5 Weighted Intuitive Fuzzy Decision Matrix Table 6. Positive and Negative Ideal Solutions of Intuitive Fuzzy Concepts

[0119] Finally, the separation degree between the emission reduction path and the positive and negative ideal solutions is calculated, and the relative proximity of each emission reduction path is calculated. The optimal selection of emission reduction paths is completed by ranking them, as detailed in Table 7. Table 7. Relative proximity and ranking of carbon reduction technologies <![CDATA[d + ]]> <![CDATA[d - ]]> r Sort Electricity Substitution A1 0.0732 0.5611 0.8846 3 Carbon capture A2 0.0735 0.5599 0.8839 4 CCER Project A3 0.0393 0.5624 0.9347 2 Carbon reduction technology A4 0.0386 0.5609 0.9356 1 Carbon Sequestration A5 0.0779 0.5597 0.8778 5 .

[0120] As shown in the table, among the five commonly used emission reduction pathways, carbon reduction technology (A4) has the greatest emission reduction potential and the highest relative closeness to the positive ideal solution, with an r of 0.9356. This technology can be regarded as the optimal solution for emission reduction. On the contrary, carbon sink (A5) has a smaller emission reduction potential, lower adoption rate and social acceptance, and the lowest relative closeness to the positive ideal solution, with an r of only 0.8778. Therefore, this technology has poor practicality.

Claims

1. A method for optimizing emission reduction paths based on information entropy, characterized in that, Includes the following steps: S1, Obtain emission reduction information to determine emission reduction path indicators, and construct a decision matrix D based on the membership degree and non-membership degree of the indicators; S2, standardize the quantitative indicator data and calculate the standardized decision matrix T; S3. Construct the first membership matrix based on the decision matrix D and the normalized decision matrix T, calculate the information entropy, and then determine the index weights. S4. Determine the intuitionistic fuzzy positive ideal solution and negative ideal solution, calculate the relative proximity between the emission reduction path and the positive ideal solution, sort the emission reduction paths according to the relative proximity and generate an optimized scheme.

2. The emission reduction path optimization method based on information entropy according to claim 1, characterized in that, Step S1 includes: S11, collect relevant information on commonly used emission reduction technologies and determine several indicators related to emission reduction pathways; S12, Construct a mapping relationship between linguistic terms and intuitive fuzzy coefficients in emission reduction path indicators; S13, construct the decision matrix D based on the intuitionistic fuzzy coefficients and calculate the uncertainty coefficients.

3. The emission reduction path optimization method based on information entropy according to claim 1 or 2, characterized in that, Step S2 specifically includes: The quantitative index data are normalized to obtain the membership degree of the quantitative index data, and the non-membership degree of the quantitative index data is calculated based on the uncertainty coefficient. Finally, the normalized decision matrix T is obtained by combining the results.

4. A method for optimizing emission reduction paths based on information entropy according to claim 1 or 2, characterized in that, Step S3 includes: S31, merge decision matrix D1 and normalized decision matrix T, and calculate the new membership contribution and non-membership contribution; S32, calculate the total contribution of multiple alternatives to this indicator; S33. The importance of indicators is determined based on information entropy, and the corresponding indicator weights are calculated based on the importance of indicators.

5. The emission reduction path optimization method based on information entropy according to claim 1, characterized in that, Step S4 includes: S41, determine the positive and negative ideal solutions of intuitionistic fuzzy based on the weighted intuitionistic fuzzy values; S42, calculate the separation degree between the emission reduction path and the positive and negative ideal solutions; S44. Calculate the relative proximity between the emission reduction path and the positive ideal solution, and sort the emission reduction paths from largest to smallest according to the relative proximity.

6. The emission reduction path optimization method based on information entropy according to claim 3, characterized in that, The membership degree and non-membership degree of the quantitative index data are respectively expressed as follows: i = 1, 2, ..., m; j = 1, 2, ..., n v ij =1-u ij In the formula: x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of quantitative indicators, respectively.

7. The emission reduction path optimization method based on information entropy according to claim 4, characterized in that, The weight of the indicator is expressed as follows: Where, d i =1-E i d i For the i-th index C i The importance of the indicator, E i For the i-th index C i Information entropy, expressed as: Where, p ij Represents the i-th attribute and the j-th scheme A. j Subordinate contribution; q ij Represents the i-th attribute and the j-th scheme A. j Non-affiliated contribution.

8. The emission reduction path optimization method based on information entropy according to claim 5, characterized in that, The intuitive fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - Represented as: Where I is the set of benefit-type attributes and J is the set of cost-type attributes.

9. A method for optimizing emission reduction paths based on information entropy according to claim 5 or 8, characterized in that, The separation degree between the emission reduction path and the positive and negative ideal solutions is expressed as: The relative proximity between the emission reduction path and the positive ideal solution is expressed as: Where, r i This refers to relative proximity.

10. An emission reduction path optimization system based on information entropy, employing the emission reduction path optimization method based on information entropy as described in any one of claims 1-8, characterized in that, include: The decision matrix construction module constructs the decision matrix D based on the membership and non-membership degrees of the indicators. The decision matrix normalization module normalizes quantitative indicator data and obtains a normalized decision matrix T. The indicator weight determination module calculates the information entropy and determines the indicator weights. The decision support module calculates the relative proximity between emission reduction paths and ideal solutions, and sorts the emission reduction paths according to the relative proximity.

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